Electricity/Solar Update

A look at the latest Jedlovec House eletricity usage and solar production from PPL and Enphase.

Load packages

library(tidyverse)
library(lubridate)
library(hms)
library(readxl)

Load PPL data

Transform PPL Data

hourly_ppl_pivot <- ppl_15mins %>% 
  rename(date = Date) %>% 
  pivot_longer(!c("Account Number", "Meter Number", date, "Read Type", Min, Max, Total), names_to = "time", values_to = "kWh") 

rename(ppl_15mins, date = Date)

hourly_ppl_pivot <- hourly_ppl_pivot %>% 
  mutate(time = parse_time(time, '%H:%M %p'), month = month(date, label=TRUE), year = year(date), yday = yday(date), wday = wday(date, label=TRUE))

(hourly_ppl_net <- hourly_ppl_pivot %>% 
  filter(`Read Type` == "kWh Net"))

Load Enphase data

import <- read_csv("enphase_history_230701.csv")

Rows: 42432 Columns: 2── Column specification ────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

update <- read_csv("hourly_generation_230625_240510.csv")

Rows: 30816 Columns: 2── Column specification ────────────────────────────────────────────────────────────────────────────────────────
Delimiter: ","
chr (1): Date/Time
dbl (1): Energy Produced (Wh)
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

update <- update %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  filter(`Date/Time` >= as.POSIXct('07/01/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) )

import <- import %>% 
  mutate(`Date/Time` = as.POSIXct(`Date/Time`,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) 

full_hist <- rbind(import,update)

full_hist %>% arrange(desc(`Date/Time`))

(hourly_production <- full_hist %>% 
  rename(datetime = `Date/Time`, energy_produced_Wh = `Energy Produced (Wh)`) %>% 
  mutate(datetime = as.POSIXct(datetime,format="%m/%d/%Y %H:%M",tz=Sys.timezone())) %>% 
  mutate(date = date(datetime), time = as.hms(format(datetime, format = "%H:%M:%S")), month = month(datetime, label=TRUE), year = year(datetime), day = day(datetime), yday = yday(datetime), monthday = format(datetime, "%m-%d"), wday = wday(datetime, label=TRUE), equinox_day = (yday + 10) %% 365, equinox_group = floor((equinox_day+15)/30)*30)
)

NA

Spot-check Enphase Should see lifetime production by day and by hour

ggplot(hourly_production, aes(datetime, energy_produced_Wh)) +
  geom_point()

ggplot(hourly_production, aes(time, energy_produced_Wh)) +
  theme(axis.text.x = element_text(angle = 90)) +
  geom_point()

Net + Produced = Consumed

Calculate production for first year of solar panels, second year, etc.

hourly_electricity <- hourly_electricity %>% 
  mutate(solar_year = factor(case_when(
                        datetime < as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 0,
                        datetime >= as.POSIXct('04/16/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone())  &
                          datetime < as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 1,
                        datetime >= as.POSIXct('04/16/2023 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone())  &
                          datetime < as.POSIXct('04/16/2024 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ~ 2,
                        TRUE ~ 3)
         )) %>% 
  mutate(yday = yday(date))

hourly_electricity %>% 
  group_by(solar_year) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

NA

Interesting! Produced kWh went down by 5%, but consumed kWh went down by 10% despite the fact that we got an electric car! Let’s explore that further.

daily_electricity <- hourly_electricity %>% 
  group_by(solar_year, yday) %>% 
  summarize(net_kWh = sum(net_kWh), produced_kWh = sum(produced_kWh), consumed_kWh = sum(consumed_kWh))

`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.

ggplot(daily_electricity, aes(yday, consumed_kWh, group=solar_year, color=solar_year)) + 
  geom_point() +
  geom_smooth(span=0.3) +
  scale_x_continuous(breaks = c(1, 32, 60, 91, 121, 152, 182, 213, 244, 274, 305, 335), labels = month.abb) +
  theme(axis.text.x = element_text(angle = 90)) 

Look how our electricity consumption got much less predictable, more uneven, after we purchased an EV and installed a level 2 charger in late January/early February 2023!

We probably need to filter out the EV charging and compare that separately.

hourly_electricity %>% summarize(min_date = min(date), max_date = max(date))

electricity_by_time <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  #filter(date <= as.POSIXct('12/31/2022 00:00',format="%m/%d/%Y %H:%M",tz=Sys.timezone()) ) %>% 
  group_by(time, solar_year) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(time)

`summarise()` has grouped output by 'time'. You can override using the `.groups` argument.

electricity_by_time

ggplot(electricity_by_time, aes(x=time, y=consumed_kWh, group=solar_year, color=solar_year)) +
  geom_point() 

  #labs(colour="",x="Time of Day",y="Electricity Consumption (kWh)")+
  #scale_color_manual(values = c("red","black","green")) +
  #ggtitle("Home Electricity in 15-Minute Intervals (Since April 15, 2022)")

Wow, look how much less was used during the day in year 2!!! Like a 50% reduction!

Is this due to milder weather?

Let’s look at it by month:

#electricity_by_month <- 
hourly_electricity %>% 
  mutate(month = month(date)) %>% 
  group_by(solar_year, month) %>% 
  summarize(consumed_kWh = mean(consumed_kWh)) %>% 
  arrange(month, solar_year) %>% 
  pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
  arrange(solar_year)

`summarise()` has grouped output by 'solar_year'. You can override using the `.groups` argument.

#Look at limited window during day (no EV charging)
 
# hourly_electricity %>% 
#   filter(time == '12:00:00') %>% 
#   #filter(time > as.POSIXct('04:00:00',format="%H:%M:$s",tz=Sys.timezone()) ) %>%  #& time < as.POSIXct('18:00',format="%H:%M",tz=Sys.timezone())) %>% 
#   mutate(month = month(date)) %>% 
#   group_by(solar_year, month) %>% 
#   summarize(consumed_kWh = mean(consumed_kWh)) %>% 
#   arrange(month, solar_year) %>% 
#   pivot_wider(names_from = month, values_from = consumed_kWh) %>% 
#   arrange(solar_year)

Look at specific months

electricity_by_month_time <- hourly_electricity %>% 
  mutate(month = as_factor(month(date))) %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  filter(month == 10) %>% 
  group_by(solar_year, month, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(solar_year, month, time)

`summarise()` has grouped output by 'solar_year', 'month'. You can override using the `.groups` argument.

ggplot(electricity_by_month_time, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Net Electricity Consumption (kWh)")

Example day with EV charging

sample_day <- hourly_electricity %>% 
  filter(solar_year == 1 | solar_year == 2) %>% 
  filter(yday == 157) %>% 
  group_by(date, solar_year, yday, time) %>% 
  summarize(produced_kWh = mean(produced_kWh), consumed_kWh = mean(consumed_kWh), net_kWh = mean(net_kWh)) %>% 
  arrange(date, solar_year, yday, time)

`summarise()` has grouped output by 'date', 'solar_year', 'yday'. You can override using the `.groups` argument.

ggplot(sample_day, aes(time, consumed_kWh, group=solar_year, color=solar_year)) +
  #facet_grid(rows = vars(month)) +
  geom_point(size=0.5) +
  ggtitle("Home Electricity in 15-Minute Intervals") +
  ylab("Electricity Consumption (kWh)")

Ok, let’s detect and remove EV charging sessions.

bcp

$x
 [1]  2.38157896  2.49495792  2.35871228  2.67047501  2.71991881  2.55883013  2.33772138  2.91051260
 [9]  2.19237889  2.74960191  1.75213595 -0.01838713  0.30820336  0.39897059  0.28664757  0.30603608
[17]  0.35919098  0.16767281  0.21823037  0.43193225  0.33928205  0.39538327  0.39027328  0.25107071
[25]  0.21501386  0.12773723  0.07036768  0.02051450  0.03319477  0.18027518  0.33188973  0.38038835
[33]  0.13785630  0.42017572  0.22867892  0.24044121  0.23030774  0.28537113  0.51695711  0.29266042
[41] -0.06083516  0.06660989  0.31487811  0.14387669  0.39981560  0.30094562  0.09083242  0.35589074
[49]  0.04101847  0.13585666  0.01485267  0.42753704  0.27042197  0.09039031  0.52307131  0.18050086
[57]  0.28360934  0.71996580  0.20057504  0.35998894  0.47077430  0.21099498  0.32290440  0.14337854
[65]  0.29055250 -0.07771297  0.31868915  0.43214999  0.21821496  0.20267917  0.22137171  0.19991720
[73]  0.15276440  0.48992731  0.27547768  0.13486293  0.19747790  0.15852249  0.03325269  0.15447847
[81] -0.02821081  0.06667815 -0.20491370  0.13184547  0.08786327  0.25226203  0.02591429  0.24308958
[89]  0.37162490  0.15715362  0.12179673 -0.14603414  0.20990544  0.38083345  0.02071311  0.26131271
[97]  0.07817865

$max_p
           [,1] [,2] [,3]
 [1,] 0.9580194    0    0
 [2,] 0.9364674    0    1
 [3,] 0.9291162    0    2
 [4,] 0.9265527    0    3
 [5,] 0.9265851    0    4
 [6,] 0.9267314    0    5
 [7,] 0.9319284    0    6
 [8,] 0.9302070    0    7
 [9,] 0.9382416    0    8
[10,] 0.9188706    0    9
[11,] 0.4892223    0   10
[12,] 0.6201123   11    0
[13,] 0.8095788   11    1
[14,] 0.7979013   11    2
[15,] 0.7929583   11    3
[16,] 0.7921342   11    4
[17,] 0.7971379   11    5
[18,] 0.8047930   11    6
[19,] 0.8080573   11    7
[20,] 0.8163079   11    8
[21,] 0.8221754   11    9
[22,] 0.8283082   11   10
[23,] 0.8366701   11   11
[24,] 0.8438057   11   12
[25,] 0.8481012   11   13
[26,] 0.8504027   11   14
[27,] 0.8507848   11   15
[28,] 0.8547635   11   16
[29,] 0.8662926   11   17
[30,] 0.8722353   11   18
[31,] 0.8742465   11   19
[32,] 0.8783681   11   20
[33,] 0.8776178   11   21
[34,] 0.8835105   11   22
[35,] 0.8870574   11   23
[36,] 0.8896860   11   24
[37,] 0.8914833   11   25
[38,] 0.8796208   11   26
[39,] 0.8882480   11   27
[40,] 0.8727340   11   28
[41,] 0.8822929   11   29
[42,] 0.8917103   11   30
[43,] 0.8947029   11   31
[44,] 0.8936474   11   32
[45,] 0.8971827   11   33
[46,] 0.8957289   11   34
[47,] 0.8977034   11   35
[48,] 0.8935607   11   36
[49,] 0.8989002   11   37
[50,] 0.8934545   11   38
[51,] 0.8949324   11   39
[52,] 0.9020641   11   40
[53,] 0.9011736   11   41
[54,] 0.8869148   11   42
[55,] 0.8999069   11   43
[56,] 0.9039034   11   44
[57,] 0.8233297   11   45
[58,] 0.8803324   11   46
[59,] 0.8933737   11   47
[60,] 0.8894446   11   48
[61,] 0.8983125   11   49
[62,] 0.9004938   11   50
[63,] 0.9002783   11   51
[64,] 0.9025502   11   52
[65,] 0.8758334   11   53
[66,] 0.8969529   11   54
[67,] 0.8960692   11   55
[68,] 0.9020904   11   56
[69,] 0.9038043   11   57
[70,] 0.9048967   11   58
[71,] 0.9052346   11   59
[72,] 0.9045400   11   60
[73,] 0.8937625   11   61
[74,] 0.9026525   11   62
[75,] 0.9025963   11   63
[76,] 0.9050601   11   64
[77,] 0.9050209   11   65
[78,] 0.8969872   11   66
[79,] 0.9041006   11   67
[80,] 0.8884811   11   68
[81,] 0.8971434   11   69
[82,] 0.8047584   11   70
[83,] 0.8785588   11   71
[84,] 0.8964911   11   72
[85,] 0.9082148   11   73
[86,] 0.9023057   11   74
[87,] 0.9110744   11   75
[88,] 0.9091609   11   76
[89,] 0.9118557   11   77
[90,] 0.9116388   11   78
[91,] 0.8629416   11   79
[92,] 0.9032366   11   80
[93,] 0.9069030   11   81
[94,] 0.9035397   11   82
[95,] 0.9127871   11   83
[96,] 0.9111167   11   84
[97,] 0.0000000    0    0

$parameters
theta alpha  beta th_cp 
  0.9   1.0   1.0   0.5 

$series_length
[1] 97

$result
NULL

attr(,"class")
[1] "BayesCP"

plot(summary(bcp))

[1] "Change points"

[1] "Segments"
     begin end      mean         SD  LL of CI  UL of CI
[1,]     1  11 2.5354545 0.31740711 2.3780390 2.6928701
[2,]    12  96 0.1314471 0.09968958 0.1136615 0.1492326